It is known from the conventional arts to distribute a plurality of sensors over a plurality of corresponding localities and to collect corresponding sensor data from the distributed sensors at some kind of central entity. Usually, the latter central entity is a server that receives all the sensor data from the sensors as data sources via one or more networks and stores the received sensor data in a database. A user can then access the database for retrieving the raw data from the sensors or process the sensor data in form of various kinds of analyses or reports.
Such systems are today already ubiquitous and find their respective applications in various fields. For example, manufacturing lines are equipped with a multitude of sensors that sense various physical quantities from the individual manufacturing or conveying stages, so as to allow processing and analysis, and, with this, a corresponding control of the manufacturing process as a whole. Further, a multitude of sensors in the sense of distributed data acquisition equipment find their applications also in larger scale systems, such as railway networks, the infrastructure of modern telecommunication networks or facility management. Common to all these applications is that the many sensors of the distributed data acquisition equipment act as data sources and provide continuously, intermittedly, or in response to specific events, corresponding sensor data.
This sensor data is usually collected by means of, for example, one or more networks such as wire-less or wire-bound, short-range or long-range networks (e.g. e.g. IrDA, IEEE802.15.4, Zigbee, RF4CE, SP100, IEEE802.11, Bluetooth™, GSM, PCS, UMTS, 3GPP, LTE, WLAN, LAN, the Internet, and the like). The collected sensor data can then be stored and processed in order to analyze the data for controlling the system that is subject to observation and control by means of the distributed data acquisition equipment. For example, physical quantities sensed as figures at various points of a manufacturing line can be processed and analyzed in order to take the correct actions necessary for maintaining the manufacturing process, controlling the process, or keeping the process as such efficient and/or outside any unstable or harmful regimes.
Besides manufacturing as such, in which distributed data acquisition already took place as part of industrial automation for many years, today also other applications take advantage of distributed data acquisition and its corresponding abilities to supervise and control large systems. For example, in the case of the above-mentioned facility management, distributed data equipment can sense filling levels or consumption figures of various consumables (e.g., printer ink/toner, paper, hand towels, soap, etc.) so as to allow for an efficient management.
It is to be noted that in the above-mentioned examples, the number of individual sensors of the distributed data acquisition equipment is usually large and so will be the amount of sensor data which needs to be stored and processed for analysis. Furthermore, the larger a system becomes that is subject to distributed data acquisition, the more likely it is that the processing of the sensor data for will become complex and, ultimately, exceeds the available processing and storage resources.
Conventional concepts usually employ one database for a specific set of sensors which provide their corresponding sensor data to that database. This database can then be accessed by some processing means for processing the data for analysis. However, these conventional systems are as such closed in the sense that both the database and the processing resources that have access to it usually belong to one owner (e.g. factory owner) so that data analysis is possible only within the realm of that owner. Although two separate owners may run similar systems, efficiency and synergy is not obtained to a satisfying extent, since each system is closed and hence superordinate or shared access is difficult and cumbersome—or simply not possible.
Furthermore, the large number of individual sensors requires specific organization schemes for structuring the large number of individual data sources in a proper and manageable fashion. From the technical point of view, the internal structure of the employed database may need to closely correlate to the chosen organization scheme in terms of the hierarchical structure and the number of implemented hierarchical levels. At the same time, however, complexity of a database usually grows with the square of the number of hierarchical levels in the chosen structure. Therefore, the technical implementation needs to reflect both the actual structure of the distributed data acquisition equipment on the hardware side and also the database complexity on the software side. The implementation of the database is thus closely related to and dependent on the structure of the distributed data acquisition equipment.
There is therefore a need for an improved system for providing sensor data analysis which allows flexible access for analysis by a plurality of users, whilst ensuring efficient technical implementation that both provides dynamic implementation and avoids database complexity exceeding an acceptable and manageable level.
From US 2013/0085719 A1 there is known a system for modeling a building automation system. However, the proposed system does not provide the necessary flexibility for providing any sensor data analysis to a plurality of users.